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Diabetes and Young Adults’ Labor Supply: Evidence from a Novel Instrumental Variable Strategy

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Abstract

This paper explores the extent to which a negative health condition limits US young adults’ participation in the labor market. We first rely on medical evidence to develop a new set of instruments for diabetes incorporating both socioeconomic and genetic information. Exploiting the variation in glycated hemoglobin (\(HbA_{1c}\)), a measure of plasma glucose concentration available in Wave IV of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we then empirically document no statistically significant effects of diabetes on employment probability among the Add Health sample. Subgroup results also yield no discernible patterns, with only some weakly significant and negative effects for the male and Hispanic subgroups. For further robustness checks, we relax an important yet untestable assumption in standard IV estimations to credibly bound the main effects of interest. By and large, the implications of diabetes on young adults’ labor supply are less pronounced than what previous research implies. Our findings complement what is known about other populations, and lend support to the protective effects of parenting and the family environment on children’s early-life labor market outcomes. To the extent that previous research has documented the negative effects of diabetes on employment among older adults, we provide some broader policy lessons that can be drawn from our IV estimates.

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Notes

  1. Seuring et al. (2019), for instance, find that the negative relationship with employment probabilities and wages appeared immediately after the diabetes diagnosis, and then again after a considerable amount of time having lived with the disease. Of note, however, the results using non-linear models were not precisely estimated (see Seuring et al. (2019), page 257).

  2. Previous studies in this literature do not distinguish labor supply from employment status, with the former being a pure supply-side phenomenon and the latter an equilibrium outcome that results from both workers’ and employers’ optimal decisions. In this study, we use the terms “labor supply” and “employment” interchangeably since, as we will argue later, our proposed instrumental variable is largely uncorrelated with unobserved determinants of either outcome.

  3. Further information on the design of each Add Health wave is available in Harris (2013). Detailed guidelines for analyzing Add Health data are available in Chen and Chantala (2014).

  4. Given the specificity of the question (which refers to diagnosis given to a respondent by a health professional, and not his or her self-assessment of the disease), we believe measurement issues are mild.

  5. As mentioned earlier, blood samples were collected during Wave IV of the Add Health survey using medical equipment (capillary whole blood spots). The two measures of glucose homeostasis, glucose and hemoglobin, were then derived based on assay of the dried blood spots. This well-controlled procedure gives us confidence in the precision of \(HbA_{1c}\) measurement.

  6. For simplicity, we further assume that diabetes is positively correlated with the errors and thus results for one-sided bounds are presented.

  7. For reference, the commonly accepted body weight classes are: normal (18.5-24.9), overweight (25-29.9), and obese (\(\ge 30\)). Source: Centers for Disease Control and Prevention. https://www.cdc.gov/obesity/adult/defining.html

  8. This additional set of results is available upon request.

  9. In Table 8 in the 7 Appendix, we perform an additional robustness check in which we interact the demographic group indicators (“Male,” “Black,” “White,” “Asian,” and “Hispanic”) with the main explanatory variable in the OLS estimation. As it turns out, neither the main nor interaction coefficients become statistically significant.

  10. The parent data files contain social, demographic, behavioral, and health data collected in 2015-2017 on a probability sample of Add Health parents who were originally interviewed in 1995 and coincide with Wave V of Add Health. Data for 2,013 Wave I parents, connected to 2,244 Add Health respondents, are available. Additionally, 988 current spouse/partner interviews are available. These data can be linked with Wave I parent data, and corresponding Add Health respondents at Waves I-V. Weight files are included.

Abbreviations

BMI:

Body mass index

HbA1c:

Gelycated hemoglobin

IV:

Instrumental variable

LTZ:

Local to zero

OLS:

Ordinary least squares

2SLS:

Two-stage least squares

US:

United States

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Acknowledgements

For the invaluable guidance, comments, and suggestions, we thank Christian Vossler (University of Tennessee), Prottoy Akbar (Aalto University and Helsinki Graduate School of Economics, Tianyi Wang (Princeton University), Quang Evansluong (Umea University and University of Gothenburg), Eric Chiang (Florida Atlantic University), Davide Dragone, Maria Bigoni, Daniele Fabbri, Francesca Barigozzi (University of Bologna), Petri Böckerman (Jyvaskyla University School of Business and Economics), workshop attendees, and all the students and faculty members of the Applied Micro Group at the University of Tennessee. We also immensely acknowledge writing support from the Department of English and Writing Center at the University of Tennessee. The Add Health dataset, whose public-use version is employed in this study, is made available for researchers via the University of Michigan’s ICPSR (Inter-university Consortium for Political and Social Science Research) portal. Information on alternative ways to access this dataset is available at: https://www.cpc.unc.edu/projects/addhealth. ICPSR public datasets are included in the University of Tennessee’s list of datasets for which no Institutional Review Board application is necessary (the full list of such datasets is available at: https://irb.utk.edu/public-use-data-sets/). We are in no way affiliated with the Add Health project or ICPSR. We declare that the views expressed here are our own and do not reflect those of our affiliated institutions or of any agencies. No financial support for this project was received. We declare no conflict of interest.

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Authors and Affiliations

  1. Centre for Health Economics, University of Gothenburg, Gothenburg, Sweden

    Paolo Nicola Barbieri

  2. Prometeia, Department of Economic Research and Analysis, Bologna, Italy

    Paolo Nicola Barbieri

  3. Department of Economics, Illinois Wesleyan University, Illinois, USA

    Hieu Nguyen

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  1. Paolo Nicola Barbieri
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Correspondence to Hieu Nguyen.

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None of us has significant competing financial, professional, or personal interests that might have influenced the work described in this manuscript.

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We do not need ethics approval from the IRB as we use publicly available data sets only and thus the research does not involve identifiable private information.

Appendix

Appendix

Table 8 Effect of diagnosed diabetes on employment probability - baseline results
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Barbieri, P.N., Nguyen, H. Diabetes and Young Adults’ Labor Supply: Evidence from a Novel Instrumental Variable Strategy. J Labor Res 43, 1–23 (2022). https://doi.org/10.1007/s12122-022-09328-z

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